Unsupervised Domain Ontology Learning from Text

Venu, Sree Harissh; Mohan, Vignesh; Urkalan, Kodaikkaavirinaadan; Geetha, T. V.

doi:10.1007/978-3-319-58130-9_13

Cited by 12 publications

(3 citation statements)

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“…Ontology building methods can be based on objective criteria, e.g., clarity, coherence, extensibility, etc. (Gruber, 1995), software engineering methods (Fernández-López, 1999), text-based construction (Zouaq & Nkambou, 2009), modular design approach (Özacar et al, 2011), ontological engineering (Suárez-Figueroa et al, 2012), unsupervised domain ontology learning method (Venu et al, 2016) , based on Formal Concept Analysis (Nong et al, 2019), etc. Let us quote some methodologies focusing on the stages which compose them.…”

Section: State Of the Artmentioning

confidence: 99%

An Ontology of Chinese Ceramic Vases

Wei

Roche

Παπαδοπούλου

et al. 2020

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Extensive collections of Chinese ceramic vases are housed in museums throughout China. They could serve as rich sources of data for historical research. Although some data sources have been digitized, the vision of heritage institutions is not only to display objects and simple descriptions (drawn from metadata) but also to allow for understanding relationships between objects (created by semantically interrelated metadata). The key to achieving this goal is to utilize the technologies of the Semantic Web, whose core is Ontology. The focus of this paper is to describe the construction of the TAO CI ("ceramics" in Chinese) ontology of the domain of ceramic vases of the Ming (1368-1644) and Qing (1644-1911) dynasties. The theoretical and methodological approach adopted to construct the TAO CI ontology is term-and-characteristic guided, i.e., it relies on a morphological analysis of the Chinese terms used in the domain, and respects the ISO principles on Terminology (ISO 1087 and 704), according to which concepts are defined by means of essential characteristics. The research presented in this article aims to publish the resulting structured data on the Semantic Web for the use of anybody interested, including museums hosting collections of these vessels, and to enrich existing methodologies on domain ontology building. To our knowledge, there are no comprehensive ontologies for Chinese ceramic vases. TAO CI ontology remedies this gap and provides a reference for ontology building in other domains of Chinese cultural heritage.

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Section: State Of the Artmentioning

confidence: 99%

An Ontology of Chinese Ceramic Vases

Wei

Roche

Παπαδοπούλου

et al. 2020

Proceedings of the 12th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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“…Venu et al [10] illustrated relation pattern hypernymy (i.e., parent-child) and meronyms (i.e., part-whole) in their system to learn ontology automatically. The proposed system developed an ontology in five stages, as follows: (1) in the first stage, an iterative focused crawler is used over the corpus collection; (2) in the second stage, the dominant terms are extracted using a hyperlink-induced topic search algorithm [11]; (3) in the third stage, hypernym and meronym patterns are extracted to recognize taxonomic relations (superclass and subclass); (4) association rules are used for mining nontaxonomic relations in the fourth stage; (5) the last stage refines the domain-specific ontology.…”

Section: A Textual-based Ol Techniquesmentioning

confidence: 99%

Using Combined List Hierarchy and Headings of HTML Documents for Learning Domain-Specific Ontology

Raza¹,

Raza²,

Jabeen³

et al. 2020

IJACSA

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HTML pages contain unstructured and diverse information. However, these documents lack semantics and are not machine understandable. Semantic webs aim to add formal semantics to web data, whereas ontology provides formal semantics to a domain and is thus considered a foundation of semantic webs. Domain ontologies can be constructed manually, but this process is tedious and inefficient. Thus, this study presents an ontology learning (OL) model to create domain ontologies automatically from a set of HTML pages. The key insight of this research is that it combines the list structure and headings of HTML pages to recognize the ontology vocabulary. The approach also incorporates synonym relationships with ontology and allows the semantic interpretation of ontology concepts. We implement the proposed OL approach to build sports ontology from a collection of sports domain HTML documents. The new sports ontology is tested using FaCT++ reasoner; results show no inconsistency in the ontology. Furthermore, experts evaluate the successful mapping of HTML lists and headings to the ontology vocabulary. The proposed OL approach performs effectively and achieves 92.7% and 95.4% precision values for list and heading mapping, respectively.

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“…With the passage of time, the need for ontologies to aid the semantic processing of documents has become more and more relevant. To quickly generate new ontologies at a low cost, several automatic ontology-learning methods [5][6][7][8][9] were introduced to extract knowledge from natural language text documents. Despite great advances in this research field, the use of these methods may result in the generation of inconsistencies and low-quality ontologies.…”

Section: Introductionmentioning

confidence: 99%